1 files changed, 95 insertions, 0 deletions
diff --git a/gnuradio-examples/python/pfb/resampler.py b/gnuradio-examples/python/pfb/resampler.py
new file mode 100755
index 000000000..6be7cf14e
--- /dev/null
+++ b/gnuradio-examples/python/pfb/resampler.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+
+from gnuradio import gr, blks2
+import scipy, pylab
+
+class mytb(gr.top_block):
+    def __init__(self, fs_in, fs_out, fc, N=10000):
+        gr.top_block.__init__(self)
+        
+        rerate = float(fs_out) / float(fs_in)
+        print "Resampling from %f to %f by %f " %(fs_in, fs_out, rerate)
+
+        # Creating our own taps
+        taps = gr.firdes.low_pass_2(32, 32, 0.25, 0.1, 80)
+
+        self.src = gr.sig_source_c(fs_in, gr.GR_SIN_WAVE, fc, 1)
+        #self.src = gr.noise_source_c(gr.GR_GAUSSIAN, 1)
+        self.head = gr.head(gr.sizeof_gr_complex, N)
+
+        # A resampler with our taps
+        self.resamp_0 = blks2.pfb_arb_resampler_ccf(rerate, taps,
+                                                    flt_size=32)
+
+        # A resampler that just needs a resampling rate.
+        # Filter is created for us and designed to cover
+        # entire bandwidth of the input signal.
+        # An optional atten=XX rate can be used here to 
+        # specify the out-of-band rejection (default=80).
+        self.resamp_1 = blks2.pfb_arb_resampler_ccf(rerate)
+
+        self.snk_in = gr.vector_sink_c()
+        self.snk_0 = gr.vector_sink_c()
+        self.snk_1 = gr.vector_sink_c()
+
+        self.connect(self.src, self.head, self.snk_in)
+        self.connect(self.head, self.resamp_0, self.snk_0)
+        self.connect(self.head, self.resamp_1, self.snk_1)
+
+def main():
+    fs_in = 8000
+    fs_out = 20000
+    fc = 1000
+    N = 10000
+
+    tb = mytb(fs_in, fs_out, fc, N)
+    tb.run()
+
+
+    # Plot PSD of signals
+    nfftsize = 2048
+    fig1 = pylab.figure(1, figsize=(10,10), facecolor="w")
+    sp1 = fig1.add_subplot(2,1,1)
+    sp1.psd(tb.snk_in.data(), NFFT=nfftsize,
+            noverlap=nfftsize/4, Fs = fs_in)
+    sp1.set_title(("Input Signal at f_s=%.2f kHz" % (fs_in/1000.0)))
+    sp1.set_xlim([-fs_in/2, fs_in/2])
+
+    sp2 = fig1.add_subplot(2,1,2)
+    sp2.psd(tb.snk_0.data(), NFFT=nfftsize,
+            noverlap=nfftsize/4, Fs = fs_out,
+            label="With our filter")
+    sp2.psd(tb.snk_1.data(), NFFT=nfftsize,
+            noverlap=nfftsize/4, Fs = fs_out,
+            label="With auto-generated filter")
+    sp2.set_title(("Output Signals at f_s=%.2f kHz" % (fs_out/1000.0)))
+    sp2.set_xlim([-fs_out/2, fs_out/2])
+    sp2.legend()
+
+    # Plot signals in time
+    Ts_in = 1.0/fs_in
+    Ts_out = 1.0/fs_out
+    t_in = scipy.arange(0, len(tb.snk_in.data())*Ts_in, Ts_in)
+    t_out = scipy.arange(0, len(tb.snk_0.data())*Ts_out, Ts_out)
+
+    fig2 = pylab.figure(2, figsize=(10,10), facecolor="w")
+    sp21 = fig2.add_subplot(2,1,1)
+    sp21.plot(t_in, tb.snk_in.data())
+    sp21.set_title(("Input Signal at f_s=%.2f kHz" % (fs_in/1000.0)))
+    sp21.set_xlim([t_in[100], t_in[200]])
+
+    sp22 = fig2.add_subplot(2,1,2)
+    sp22.plot(t_out, tb.snk_0.data(),
+              label="With our filter")
+    sp22.plot(t_out, tb.snk_1.data(),
+              label="With auto-generated filter")
+    sp22.set_title(("Output Signals at f_s=%.2f kHz" % (fs_out/1000.0)))
+    r = float(fs_out)/float(fs_in)
+    sp22.set_xlim([t_out[r * 100], t_out[r * 200]])
+    sp22.legend()
+
+    pylab.show()
+
+if __name__ == "__main__":
+    main()
+